Overview

Dataset statistics

Number of variables32
Number of observations1163265
Missing cells593331
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory284.0 MiB
Average record size in memory256.0 B

Variable types

Numeric7
DateTime3
Categorical13
Text9

Alerts

agency has constant value ""Constant
agency_name has constant value ""Constant
complaint_type has constant value ""Constant
location_type has constant value ""Constant
address_type is highly overall correlated with city and 1 other fieldsHigh correlation
bbl is highly overall correlated with borough and 3 other fieldsHigh correlation
borough is highly overall correlated with bbl and 2 other fieldsHigh correlation
city is highly overall correlated with address_type and 3 other fieldsHigh correlation
incident_zip is highly overall correlated with bbl and 1 other fieldsHigh correlation
latitude is highly overall correlated with park_borough and 1 other fieldsHigh correlation
longitude is highly overall correlated with park_borough and 1 other fieldsHigh correlation
park_borough is highly overall correlated with bbl and 5 other fieldsHigh correlation
park_facility_name is highly overall correlated with address_typeHigh correlation
x_coordinate_state_plane is highly overall correlated with longitudeHigh correlation
y_coordinate_state_plane is highly overall correlated with latitudeHigh correlation
address_type is highly imbalanced (79.4%)Imbalance
status is highly imbalanced (99.8%)Imbalance
park_facility_name is highly imbalanced (> 99.9%)Imbalance
location_type has 11886 (1.0%) missing valuesMissing
address_type has 215156 (18.5%) missing valuesMissing
city has 83650 (7.2%) missing valuesMissing
landmark has 83662 (7.2%) missing valuesMissing
bbl has 141586 (12.2%) missing valuesMissing
unique_key has unique valuesUnique

Reproduction

Analysis started2023-12-05 19:12:15.960332
Analysis finished2023-12-05 19:23:24.590273
Duration11 minutes and 8.63 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

unique_key
Real number (ℝ)

UNIQUE 

Distinct1163265
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55011271
Minimum48536309
Maximum59631559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-12-05T14:23:24.767693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum48536309
5-th percentile50127161
Q152690582
median55269127
Q357474905
95-th percentile59196675
Maximum59631559
Range11095250
Interquartile range (IQR)4784323

Descriptive statistics

Standard deviation2875076.4
Coefficient of variation (CV)0.052263406
Kurtosis-1.067847
Mean55011271
Median Absolute Deviation (MAD)2371015
Skewness-0.22653672
Sum6.3992687 × 1013
Variance8.2660643 × 1012
MonotonicityNot monotonic
2023-12-05T14:23:25.034156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52934348 1
 
< 0.1%
59243775 1
 
< 0.1%
59241553 1
 
< 0.1%
59244454 1
 
< 0.1%
59243071 1
 
< 0.1%
59242997 1
 
< 0.1%
59243070 1
 
< 0.1%
59247340 1
 
< 0.1%
59243103 1
 
< 0.1%
59245851 1
 
< 0.1%
Other values (1163255) 1163255
> 99.9%
ValueCountFrequency (%)
48536309 1
< 0.1%
48536312 1
< 0.1%
48536313 1
< 0.1%
48536323 1
< 0.1%
48536331 1
< 0.1%
48536868 1
< 0.1%
48536893 1
< 0.1%
48536901 1
< 0.1%
48536945 1
< 0.1%
48537147 1
< 0.1%
ValueCountFrequency (%)
59631559 1
< 0.1%
59631556 1
< 0.1%
59631552 1
< 0.1%
59631551 1
< 0.1%
59631550 1
< 0.1%
59631549 1
< 0.1%
59631521 1
< 0.1%
59631518 1
< 0.1%
59631517 1
< 0.1%
59631516 1
< 0.1%
Distinct1154011
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Minimum2021-01-01 00:03:03
Maximum2023-12-04 01:28:04
2023-12-05T14:23:25.306324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:25.875162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1154198
Distinct (%)99.2%
Missing190
Missing (%)< 0.1%
Memory size8.9 MiB
Minimum2021-01-01 00:47:09
Maximum2023-12-04 01:31:20
2023-12-05T14:23:26.288809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:26.578084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

agency
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
NYPD
1163265 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4653060
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNYPD
2nd rowNYPD
3rd rowNYPD
4th rowNYPD
5th rowNYPD

Common Values

ValueCountFrequency (%)
NYPD 1163265
100.0%

Length

2023-12-05T14:23:26.854669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:27.035230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
nypd 1163265
100.0%

Most occurring characters

ValueCountFrequency (%)
N 1163265
25.0%
Y 1163265
25.0%
P 1163265
25.0%
D 1163265
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4653060
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1163265
25.0%
Y 1163265
25.0%
P 1163265
25.0%
D 1163265
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4653060
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1163265
25.0%
Y 1163265
25.0%
P 1163265
25.0%
D 1163265
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4653060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1163265
25.0%
Y 1163265
25.0%
P 1163265
25.0%
D 1163265
25.0%

agency_name
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
New York City Police Department
1163265 

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters36061215
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York City Police Department
2nd rowNew York City Police Department
3rd rowNew York City Police Department
4th rowNew York City Police Department
5th rowNew York City Police Department

Common Values

ValueCountFrequency (%)
New York City Police Department 1163265
100.0%

Length

2023-12-05T14:23:27.242532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:27.424006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
new 1163265
20.0%
york 1163265
20.0%
city 1163265
20.0%
police 1163265
20.0%
department 1163265
20.0%

Most occurring characters

ValueCountFrequency (%)
4653060
 
12.9%
e 4653060
 
12.9%
t 3489795
 
9.7%
o 2326530
 
6.5%
r 2326530
 
6.5%
i 2326530
 
6.5%
l 1163265
 
3.2%
m 1163265
 
3.2%
a 1163265
 
3.2%
p 1163265
 
3.2%
Other values (10) 11632650
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25591830
71.0%
Uppercase Letter 5816325
 
16.1%
Space Separator 4653060
 
12.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4653060
18.2%
t 3489795
13.6%
o 2326530
9.1%
r 2326530
9.1%
i 2326530
9.1%
l 1163265
 
4.5%
m 1163265
 
4.5%
a 1163265
 
4.5%
p 1163265
 
4.5%
c 1163265
 
4.5%
Other values (4) 4653060
18.2%
Uppercase Letter
ValueCountFrequency (%)
D 1163265
20.0%
N 1163265
20.0%
P 1163265
20.0%
C 1163265
20.0%
Y 1163265
20.0%
Space Separator
ValueCountFrequency (%)
4653060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31408155
87.1%
Common 4653060
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4653060
14.8%
t 3489795
 
11.1%
o 2326530
 
7.4%
r 2326530
 
7.4%
i 2326530
 
7.4%
l 1163265
 
3.7%
m 1163265
 
3.7%
a 1163265
 
3.7%
p 1163265
 
3.7%
D 1163265
 
3.7%
Other values (9) 10469385
33.3%
Common
ValueCountFrequency (%)
4653060
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36061215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4653060
 
12.9%
e 4653060
 
12.9%
t 3489795
 
9.7%
o 2326530
 
6.5%
r 2326530
 
6.5%
i 2326530
 
6.5%
l 1163265
 
3.2%
m 1163265
 
3.2%
a 1163265
 
3.2%
p 1163265
 
3.2%
Other values (10) 11632650
32.3%

complaint_type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Illegal Parking
1163265 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters17448975
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIllegal Parking
2nd rowIllegal Parking
3rd rowIllegal Parking
4th rowIllegal Parking
5th rowIllegal Parking

Common Values

ValueCountFrequency (%)
Illegal Parking 1163265
100.0%

Length

2023-12-05T14:23:27.617544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:27.798320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
illegal 1163265
50.0%
parking 1163265
50.0%

Most occurring characters

ValueCountFrequency (%)
l 3489795
20.0%
g 2326530
13.3%
a 2326530
13.3%
I 1163265
 
6.7%
e 1163265
 
6.7%
1163265
 
6.7%
P 1163265
 
6.7%
r 1163265
 
6.7%
k 1163265
 
6.7%
i 1163265
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13959180
80.0%
Uppercase Letter 2326530
 
13.3%
Space Separator 1163265
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 3489795
25.0%
g 2326530
16.7%
a 2326530
16.7%
e 1163265
 
8.3%
r 1163265
 
8.3%
k 1163265
 
8.3%
i 1163265
 
8.3%
n 1163265
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
I 1163265
50.0%
P 1163265
50.0%
Space Separator
ValueCountFrequency (%)
1163265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16285710
93.3%
Common 1163265
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 3489795
21.4%
g 2326530
14.3%
a 2326530
14.3%
I 1163265
 
7.1%
e 1163265
 
7.1%
P 1163265
 
7.1%
r 1163265
 
7.1%
k 1163265
 
7.1%
i 1163265
 
7.1%
n 1163265
 
7.1%
Common
ValueCountFrequency (%)
1163265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17448975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 3489795
20.0%
g 2326530
13.3%
a 2326530
13.3%
I 1163265
 
6.7%
e 1163265
 
6.7%
1163265
 
6.7%
P 1163265
 
6.7%
r 1163265
 
6.7%
k 1163265
 
6.7%
i 1163265
 
6.7%

descriptor
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Blocked Hydrant
355938 
Posted Parking Sign Violation
272518 
Blocked Sidewalk
135647 
Commercial Overnight Parking
102751 
Double Parked Blocking Traffic
70217 
Other values (9)
226194 

Length

Max length30
Median length29
Mean length21.728514
Min length15

Characters and Unicode

Total characters25276020
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlocked Hydrant
2nd rowBlocked Hydrant
3rd rowBlocked Hydrant
4th rowBlocked Hydrant
5th rowBlocked Hydrant

Common Values

ValueCountFrequency (%)
Blocked Hydrant 355938
30.6%
Posted Parking Sign Violation 272518
23.4%
Blocked Sidewalk 135647
 
11.7%
Commercial Overnight Parking 102751
 
8.8%
Double Parked Blocking Traffic 70217
 
6.0%
Blocked Bike Lane 60160
 
5.2%
Blocked Crosswalk 51168
 
4.4%
Double Parked Blocking Vehicle 38859
 
3.3%
Parking Permit Improper Use 29506
 
2.5%
Paper License Plates 19102
 
1.6%
Other values (4) 27399
 
2.4%

Length

2023-12-05T14:23:28.022051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blocked 602913
18.0%
parking 404775
12.1%
hydrant 355938
10.6%
posted 272518
 
8.1%
sign 272518
 
8.1%
violation 272518
 
8.1%
sidewalk 135647
 
4.0%
commercial 109724
 
3.3%
overnight 109724
 
3.3%
double 109076
 
3.3%
Other values (21) 708460
21.1%

Most occurring characters

ValueCountFrequency (%)
2190546
 
8.7%
i 1924770
 
7.6%
e 1871628
 
7.4%
o 1860350
 
7.4%
a 1651337
 
6.5%
n 1619906
 
6.4%
d 1496518
 
5.9%
k 1472815
 
5.8%
l 1456329
 
5.8%
r 1362152
 
5.4%
Other values (26) 8369669
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19731663
78.1%
Uppercase Letter 3353811
 
13.3%
Space Separator 2190546
 
8.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1924770
9.8%
e 1871628
9.5%
o 1860350
9.4%
a 1651337
8.4%
n 1619906
8.2%
d 1496518
 
7.6%
k 1472815
 
7.5%
l 1456329
 
7.4%
r 1362152
 
6.9%
t 1086705
 
5.5%
Other values (13) 3929153
19.9%
Uppercase Letter
ValueCountFrequency (%)
P 857994
25.6%
B 784329
23.4%
S 415138
12.4%
H 355938
10.6%
V 311377
 
9.3%
C 160892
 
4.8%
O 113639
 
3.4%
D 113407
 
3.4%
L 95357
 
2.8%
T 74548
 
2.2%
Other values (2) 71192
 
2.1%
Space Separator
ValueCountFrequency (%)
2190546
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23085474
91.3%
Common 2190546
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1924770
 
8.3%
e 1871628
 
8.1%
o 1860350
 
8.1%
a 1651337
 
7.2%
n 1619906
 
7.0%
d 1496518
 
6.5%
k 1472815
 
6.4%
l 1456329
 
6.3%
r 1362152
 
5.9%
t 1086705
 
4.7%
Other values (25) 7282964
31.5%
Common
ValueCountFrequency (%)
2190546
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25276020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2190546
 
8.7%
i 1924770
 
7.6%
e 1871628
 
7.4%
o 1860350
 
7.4%
a 1651337
 
6.5%
n 1619906
 
6.4%
d 1496518
 
5.9%
k 1472815
 
5.8%
l 1456329
 
5.8%
r 1362152
 
5.4%
Other values (26) 8369669
33.1%

location_type
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing11886
Missing (%)1.0%
Memory size8.9 MiB
Street/Sidewalk
1151379 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters17270685
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStreet/Sidewalk
2nd rowStreet/Sidewalk
3rd rowStreet/Sidewalk
4th rowStreet/Sidewalk
5th rowStreet/Sidewalk

Common Values

ValueCountFrequency (%)
Street/Sidewalk 1151379
99.0%
(Missing) 11886
 
1.0%

Length

2023-12-05T14:23:28.258111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:28.459122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
street/sidewalk 1151379
100.0%

Most occurring characters

ValueCountFrequency (%)
e 3454137
20.0%
S 2302758
13.3%
t 2302758
13.3%
r 1151379
 
6.7%
/ 1151379
 
6.7%
i 1151379
 
6.7%
d 1151379
 
6.7%
w 1151379
 
6.7%
a 1151379
 
6.7%
l 1151379
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13816548
80.0%
Uppercase Letter 2302758
 
13.3%
Other Punctuation 1151379
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3454137
25.0%
t 2302758
16.7%
r 1151379
 
8.3%
i 1151379
 
8.3%
d 1151379
 
8.3%
w 1151379
 
8.3%
a 1151379
 
8.3%
l 1151379
 
8.3%
k 1151379
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
S 2302758
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1151379
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16119306
93.3%
Common 1151379
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3454137
21.4%
S 2302758
14.3%
t 2302758
14.3%
r 1151379
 
7.1%
i 1151379
 
7.1%
d 1151379
 
7.1%
w 1151379
 
7.1%
a 1151379
 
7.1%
l 1151379
 
7.1%
k 1151379
 
7.1%
Common
ValueCountFrequency (%)
/ 1151379
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17270685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3454137
20.0%
S 2302758
13.3%
t 2302758
13.3%
r 1151379
 
6.7%
/ 1151379
 
6.7%
i 1151379
 
6.7%
d 1151379
 
6.7%
w 1151379
 
6.7%
a 1151379
 
6.7%
l 1151379
 
6.7%

incident_zip
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)< 0.1%
Missing1208
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10940.354
Minimum10000
Maximum12345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-12-05T14:23:28.643830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10019
Q110463
median11212
Q311355
95-th percentile11421
Maximum12345
Range2345
Interquartile range (IQR)892

Descriptive statistics

Standard deviation501.77467
Coefficient of variation (CV)0.045864573
Kurtosis-0.98870764
Mean10940.354
Median Absolute Deviation (MAD)163
Skewness-0.77192328
Sum1.2713315 × 1010
Variance251777.82
MonotonicityNot monotonic
2023-12-05T14:23:28.917952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11385 33965
 
2.9%
11201 28724
 
2.5%
10468 22371
 
1.9%
11378 19518
 
1.7%
11101 18430
 
1.6%
11214 16823
 
1.4%
11205 16127
 
1.4%
11208 15750
 
1.4%
11230 15622
 
1.3%
11223 15341
 
1.3%
Other values (221) 959386
82.5%
ValueCountFrequency (%)
10000 49
 
< 0.1%
10001 4476
0.4%
10002 9451
0.8%
10003 3856
0.3%
10004 1220
 
0.1%
10005 565
 
< 0.1%
10006 788
 
0.1%
10007 2252
 
0.2%
10009 4382
0.4%
10010 1842
 
0.2%
ValueCountFrequency (%)
12345 24
 
< 0.1%
11697 51
 
< 0.1%
11695 3
 
< 0.1%
11694 6011
0.5%
11693 3002
0.3%
11692 846
 
0.1%
11691 3386
0.3%
11436 3004
0.3%
11435 5846
0.5%
11434 5255
0.5%
Distinct260910
Distinct (%)22.5%
Missing1183
Missing (%)0.1%
Memory size8.9 MiB
2023-12-05T14:23:29.368505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length38
Median length34
Mean length17.454932
Min length3

Characters and Unicode

Total characters20284062
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142618 ?
Unique (%)12.3%

Sample

1st row8700 23 AVENUE
2nd row69-41 64 STREET
3rd row69-23 64 STREET
4th row64 STREET
5th row1577 LELAND AVENUE
ValueCountFrequency (%)
street 534721
 
14.6%
avenue 417626
 
11.4%
east 94884
 
2.6%
west 78841
 
2.2%
place 48539
 
1.3%
boulevard 39943
 
1.1%
road 39797
 
1.1%
parkway 13764
 
0.4%
drive 12633
 
0.3%
grand 12060
 
0.3%
Other values (28010) 2360161
64.6%
2023-12-05T14:23:30.105841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2781421
 
13.7%
E 2702884
 
13.3%
T 1543891
 
7.6%
A 1107709
 
5.5%
R 1090169
 
5.4%
S 993814
 
4.9%
1 910498
 
4.5%
N 859748
 
4.2%
2 624439
 
3.1%
U 587934
 
2.9%
Other values (32) 7081555
34.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12349238
60.9%
Decimal Number 4849589
 
23.9%
Space Separator 2781421
 
13.7%
Dash Punctuation 303474
 
1.5%
Other Punctuation 338
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2702884
21.9%
T 1543891
12.5%
A 1107709
9.0%
R 1090169
8.8%
S 993814
 
8.0%
N 859748
 
7.0%
U 587934
 
4.8%
V 528012
 
4.3%
O 479392
 
3.9%
L 366555
 
3.0%
Other values (16) 2089130
16.9%
Decimal Number
ValueCountFrequency (%)
1 910498
18.8%
2 624439
12.9%
3 498228
10.3%
0 492599
10.2%
5 473540
9.8%
4 440413
9.1%
6 378994
7.8%
7 369157
7.6%
8 333683
 
6.9%
9 328038
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/ 210
62.1%
' 128
37.9%
Space Separator
ValueCountFrequency (%)
2781421
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 303474
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12349238
60.9%
Common 7934824
39.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2702884
21.9%
T 1543891
12.5%
A 1107709
9.0%
R 1090169
8.8%
S 993814
 
8.0%
N 859748
 
7.0%
U 587934
 
4.8%
V 528012
 
4.3%
O 479392
 
3.9%
L 366555
 
3.0%
Other values (16) 2089130
16.9%
Common
ValueCountFrequency (%)
2781421
35.1%
1 910498
 
11.5%
2 624439
 
7.9%
3 498228
 
6.3%
0 492599
 
6.2%
5 473540
 
6.0%
4 440413
 
5.6%
6 378994
 
4.8%
7 369157
 
4.7%
8 333683
 
4.2%
Other values (6) 631852
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20284062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2781421
 
13.7%
E 2702884
 
13.3%
T 1543891
 
7.6%
A 1107709
 
5.5%
R 1090169
 
5.4%
S 993814
 
4.9%
1 910498
 
4.5%
N 859748
 
4.2%
2 624439
 
3.1%
U 587934
 
2.9%
Other values (32) 7081555
34.9%
Distinct8069
Distinct (%)0.7%
Missing1183
Missing (%)0.1%
Memory size8.9 MiB
2023-12-05T14:23:30.533133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.029777
Min length3

Characters and Unicode

Total characters15141669
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1228 ?
Unique (%)0.1%

Sample

1st row23 AVENUE
2nd row64 STREET
3rd row64 STREET
4th row64 STREET
5th rowLELAND AVENUE
ValueCountFrequency (%)
street 534721
20.8%
avenue 417626
 
16.2%
east 94884
 
3.7%
west 78841
 
3.1%
place 48539
 
1.9%
boulevard 39943
 
1.6%
road 39797
 
1.5%
parkway 13764
 
0.5%
drive 12633
 
0.5%
grand 12060
 
0.5%
Other values (4511) 1282014
49.8%
2023-12-05T14:23:31.245759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2702394
17.8%
1703274
11.2%
T 1543874
10.2%
A 1104984
 
7.3%
R 1090146
 
7.2%
S 993795
 
6.6%
N 859728
 
5.7%
U 587934
 
3.9%
V 528006
 
3.5%
O 479349
 
3.2%
Other values (31) 3548185
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12344397
81.5%
Space Separator 1703274
 
11.2%
Decimal Number 1093777
 
7.2%
Other Punctuation 128
 
< 0.1%
Dash Punctuation 91
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2702394
21.9%
T 1543874
12.5%
A 1104984
9.0%
R 1090146
8.8%
S 993795
 
8.1%
N 859728
 
7.0%
U 587934
 
4.8%
V 528006
 
4.3%
O 479349
 
3.9%
L 366549
 
3.0%
Other values (16) 2087638
16.9%
Decimal Number
ValueCountFrequency (%)
1 220164
20.1%
2 128323
11.7%
3 110593
10.1%
7 100729
9.2%
6 98942
9.0%
5 94950
8.7%
4 91415
8.4%
8 88211
8.1%
9 85791
 
7.8%
0 74659
 
6.8%
Space Separator
ValueCountFrequency (%)
1703274
100.0%
Other Punctuation
ValueCountFrequency (%)
' 128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 91
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12344397
81.5%
Common 2797272
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2702394
21.9%
T 1543874
12.5%
A 1104984
9.0%
R 1090146
8.8%
S 993795
 
8.1%
N 859728
 
7.0%
U 587934
 
4.8%
V 528006
 
4.3%
O 479349
 
3.9%
L 366549
 
3.0%
Other values (16) 2087638
16.9%
Common
ValueCountFrequency (%)
1703274
60.9%
1 220164
 
7.9%
2 128323
 
4.6%
3 110593
 
4.0%
7 100729
 
3.6%
6 98942
 
3.5%
5 94950
 
3.4%
4 91415
 
3.3%
8 88211
 
3.2%
9 85791
 
3.1%
Other values (5) 74880
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15141669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2702394
17.8%
1703274
11.2%
T 1543874
10.2%
A 1104984
 
7.3%
R 1090146
 
7.2%
S 993795
 
6.6%
N 859728
 
5.7%
U 587934
 
3.9%
V 528006
 
3.5%
O 479349
 
3.2%
Other values (31) 3548185
23.4%
Distinct8587
Distinct (%)0.7%
Missing2653
Missing (%)0.2%
Memory size8.9 MiB
2023-12-05T14:23:31.708337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length29
Mean length12.8733
Min length3

Characters and Unicode

Total characters14940906
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1042 ?
Unique (%)0.1%

Sample

1st rowBENSON AVENUE
2nd rowCATALPA AVENUE
3rd rowCATALPA AVENUE
4th row64 STREET
5th rowGUERLAIN STREET
ValueCountFrequency (%)
avenue 524462
 
20.2%
street 391271
 
15.1%
east 81211
 
3.1%
west 61523
 
2.4%
road 45516
 
1.8%
place 44550
 
1.7%
boulevard 35770
 
1.4%
5 13995
 
0.5%
park 13841
 
0.5%
way 13840
 
0.5%
Other values (5429) 1372414
52.8%
2023-12-05T14:23:32.406657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2609486
17.5%
1644752
11.0%
A 1258359
 
8.4%
T 1245884
 
8.3%
N 977036
 
6.5%
R 946319
 
6.3%
S 824252
 
5.5%
U 688577
 
4.6%
V 616504
 
4.1%
O 476561
 
3.2%
Other values (33) 3653176
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12247302
82.0%
Space Separator 1644752
 
11.0%
Decimal Number 1044823
 
7.0%
Dash Punctuation 2562
 
< 0.1%
Other Punctuation 1437
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2609486
21.3%
A 1258359
10.3%
T 1245884
10.2%
N 977036
 
8.0%
R 946319
 
7.7%
S 824252
 
6.7%
U 688577
 
5.6%
V 616504
 
5.0%
O 476561
 
3.9%
L 403097
 
3.3%
Other values (16) 2201227
18.0%
Decimal Number
ValueCountFrequency (%)
1 220192
21.1%
2 124042
11.9%
3 101440
9.7%
7 93359
8.9%
5 92056
8.8%
6 90524
8.7%
8 85015
 
8.1%
4 83839
 
8.0%
0 77594
 
7.4%
9 76762
 
7.3%
Other Punctuation
ValueCountFrequency (%)
/ 694
48.3%
' 691
48.1%
& 52
 
3.6%
Space Separator
ValueCountFrequency (%)
1644752
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2562
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12247302
82.0%
Common 2693604
 
18.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2609486
21.3%
A 1258359
10.3%
T 1245884
10.2%
N 977036
 
8.0%
R 946319
 
7.7%
S 824252
 
6.7%
U 688577
 
5.6%
V 616504
 
5.0%
O 476561
 
3.9%
L 403097
 
3.3%
Other values (16) 2201227
18.0%
Common
ValueCountFrequency (%)
1644752
61.1%
1 220192
 
8.2%
2 124042
 
4.6%
3 101440
 
3.8%
7 93359
 
3.5%
5 92056
 
3.4%
6 90524
 
3.4%
8 85015
 
3.2%
4 83839
 
3.1%
0 77594
 
2.9%
Other values (7) 80791
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14940906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2609486
17.5%
1644752
11.0%
A 1258359
 
8.4%
T 1245884
 
8.3%
N 977036
 
6.5%
R 946319
 
6.3%
S 824252
 
5.5%
U 688577
 
4.6%
V 616504
 
4.1%
O 476561
 
3.2%
Other values (33) 3653176
24.5%
Distinct8730
Distinct (%)0.8%
Missing1992
Missing (%)0.2%
Memory size8.9 MiB
2023-12-05T14:23:32.968959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.137392
Min length3

Characters and Unicode

Total characters15256099
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1043 ?
Unique (%)0.1%

Sample

1st rowBATH AVENUE
2nd rowSHALER AVENUE
3rd rowSHALER AVENUE
4th rowSHALER AVENUE
5th rowEAST TREMONT AVENUE
ValueCountFrequency (%)
avenue 520902
 
19.9%
street 384473
 
14.7%
east 81388
 
3.1%
west 60862
 
2.3%
road 49570
 
1.9%
place 44396
 
1.7%
boulevard 41628
 
1.6%
end 14982
 
0.6%
drive 13932
 
0.5%
dead 13556
 
0.5%
Other values (5495) 1389016
53.1%
2023-12-05T14:23:33.662463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2645418
17.3%
1664653
10.9%
T 1253491
 
8.2%
A 1249377
 
8.2%
N 1004665
 
6.6%
R 983565
 
6.4%
S 827156
 
5.4%
U 706147
 
4.6%
V 640384
 
4.2%
O 532151
 
3.5%
Other values (33) 3749092
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12591932
82.5%
Space Separator 1664653
 
10.9%
Decimal Number 995560
 
6.5%
Dash Punctuation 2461
 
< 0.1%
Other Punctuation 1461
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2645418
21.0%
T 1253491
10.0%
A 1249377
9.9%
N 1004665
 
8.0%
R 983565
 
7.8%
S 827156
 
6.6%
U 706147
 
5.6%
V 640384
 
5.1%
O 532151
 
4.2%
L 436700
 
3.5%
Other values (16) 2312878
18.4%
Decimal Number
ValueCountFrequency (%)
1 219344
22.0%
2 122328
12.3%
3 99528
10.0%
5 85915
 
8.6%
7 85542
 
8.6%
6 83568
 
8.4%
4 82339
 
8.3%
8 78632
 
7.9%
9 69935
 
7.0%
0 68429
 
6.9%
Other Punctuation
ValueCountFrequency (%)
/ 717
49.1%
' 699
47.8%
& 45
 
3.1%
Space Separator
ValueCountFrequency (%)
1664653
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2461
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12591932
82.5%
Common 2664167
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2645418
21.0%
T 1253491
10.0%
A 1249377
9.9%
N 1004665
 
8.0%
R 983565
 
7.8%
S 827156
 
6.6%
U 706147
 
5.6%
V 640384
 
5.1%
O 532151
 
4.2%
L 436700
 
3.5%
Other values (16) 2312878
18.4%
Common
ValueCountFrequency (%)
1664653
62.5%
1 219344
 
8.2%
2 122328
 
4.6%
3 99528
 
3.7%
5 85915
 
3.2%
7 85542
 
3.2%
6 83568
 
3.1%
4 82339
 
3.1%
8 78632
 
3.0%
9 69935
 
2.6%
Other values (7) 72383
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15256099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2645418
17.3%
1664653
10.9%
T 1253491
 
8.2%
A 1249377
 
8.2%
N 1004665
 
6.6%
R 983565
 
6.4%
S 827156
 
5.4%
U 706147
 
4.6%
V 640384
 
4.2%
O 532151
 
3.5%
Other values (33) 3749092
24.6%
Distinct8587
Distinct (%)0.7%
Missing2653
Missing (%)0.2%
Memory size8.9 MiB
2023-12-05T14:23:34.267752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length29
Mean length12.8733
Min length3

Characters and Unicode

Total characters14940906
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1042 ?
Unique (%)0.1%

Sample

1st rowBENSON AVENUE
2nd rowCATALPA AVENUE
3rd rowCATALPA AVENUE
4th row64 STREET
5th rowGUERLAIN STREET
ValueCountFrequency (%)
avenue 524462
 
20.2%
street 391271
 
15.1%
east 81211
 
3.1%
west 61523
 
2.4%
road 45516
 
1.8%
place 44550
 
1.7%
boulevard 35770
 
1.4%
5 13995
 
0.5%
park 13841
 
0.5%
way 13840
 
0.5%
Other values (5429) 1372414
52.8%
2023-12-05T14:23:35.000328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2609486
17.5%
1644752
11.0%
A 1258359
 
8.4%
T 1245884
 
8.3%
N 977036
 
6.5%
R 946319
 
6.3%
S 824252
 
5.5%
U 688577
 
4.6%
V 616504
 
4.1%
O 476561
 
3.2%
Other values (33) 3653176
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12247302
82.0%
Space Separator 1644752
 
11.0%
Decimal Number 1044823
 
7.0%
Dash Punctuation 2562
 
< 0.1%
Other Punctuation 1437
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2609486
21.3%
A 1258359
10.3%
T 1245884
10.2%
N 977036
 
8.0%
R 946319
 
7.7%
S 824252
 
6.7%
U 688577
 
5.6%
V 616504
 
5.0%
O 476561
 
3.9%
L 403097
 
3.3%
Other values (16) 2201227
18.0%
Decimal Number
ValueCountFrequency (%)
1 220192
21.1%
2 124042
11.9%
3 101440
9.7%
7 93359
8.9%
5 92056
8.8%
6 90524
8.7%
8 85015
 
8.1%
4 83839
 
8.0%
0 77594
 
7.4%
9 76762
 
7.3%
Other Punctuation
ValueCountFrequency (%)
/ 694
48.3%
' 691
48.1%
& 52
 
3.6%
Space Separator
ValueCountFrequency (%)
1644752
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2562
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12247302
82.0%
Common 2693604
 
18.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2609486
21.3%
A 1258359
10.3%
T 1245884
10.2%
N 977036
 
8.0%
R 946319
 
7.7%
S 824252
 
6.7%
U 688577
 
5.6%
V 616504
 
5.0%
O 476561
 
3.9%
L 403097
 
3.3%
Other values (16) 2201227
18.0%
Common
ValueCountFrequency (%)
1644752
61.1%
1 220192
 
8.2%
2 124042
 
4.6%
3 101440
 
3.8%
7 93359
 
3.5%
5 92056
 
3.4%
6 90524
 
3.4%
8 85015
 
3.2%
4 83839
 
3.1%
0 77594
 
2.9%
Other values (7) 80791
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14940906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2609486
17.5%
1644752
11.0%
A 1258359
 
8.4%
T 1245884
 
8.3%
N 977036
 
6.5%
R 946319
 
6.3%
S 824252
 
5.5%
U 688577
 
4.6%
V 616504
 
4.1%
O 476561
 
3.2%
Other values (33) 3653176
24.5%
Distinct8730
Distinct (%)0.8%
Missing1992
Missing (%)0.2%
Memory size8.9 MiB
2023-12-05T14:23:35.402987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.137392
Min length3

Characters and Unicode

Total characters15256099
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1043 ?
Unique (%)0.1%

Sample

1st rowBATH AVENUE
2nd rowSHALER AVENUE
3rd rowSHALER AVENUE
4th rowSHALER AVENUE
5th rowEAST TREMONT AVENUE
ValueCountFrequency (%)
avenue 520902
 
19.9%
street 384473
 
14.7%
east 81388
 
3.1%
west 60862
 
2.3%
road 49570
 
1.9%
place 44396
 
1.7%
boulevard 41628
 
1.6%
end 14982
 
0.6%
drive 13932
 
0.5%
dead 13556
 
0.5%
Other values (5495) 1389016
53.1%
2023-12-05T14:23:36.067710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2645418
17.3%
1664653
10.9%
T 1253491
 
8.2%
A 1249377
 
8.2%
N 1004665
 
6.6%
R 983565
 
6.4%
S 827156
 
5.4%
U 706147
 
4.6%
V 640384
 
4.2%
O 532151
 
3.5%
Other values (33) 3749092
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12591932
82.5%
Space Separator 1664653
 
10.9%
Decimal Number 995560
 
6.5%
Dash Punctuation 2461
 
< 0.1%
Other Punctuation 1461
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2645418
21.0%
T 1253491
10.0%
A 1249377
9.9%
N 1004665
 
8.0%
R 983565
 
7.8%
S 827156
 
6.6%
U 706147
 
5.6%
V 640384
 
5.1%
O 532151
 
4.2%
L 436700
 
3.5%
Other values (16) 2312878
18.4%
Decimal Number
ValueCountFrequency (%)
1 219344
22.0%
2 122328
12.3%
3 99528
10.0%
5 85915
 
8.6%
7 85542
 
8.6%
6 83568
 
8.4%
4 82339
 
8.3%
8 78632
 
7.9%
9 69935
 
7.0%
0 68429
 
6.9%
Other Punctuation
ValueCountFrequency (%)
/ 717
49.1%
' 699
47.8%
& 45
 
3.1%
Space Separator
ValueCountFrequency (%)
1664653
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2461
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12591932
82.5%
Common 2664167
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2645418
21.0%
T 1253491
10.0%
A 1249377
9.9%
N 1004665
 
8.0%
R 983565
 
7.8%
S 827156
 
6.6%
U 706147
 
5.6%
V 640384
 
5.1%
O 532151
 
4.2%
L 436700
 
3.5%
Other values (16) 2312878
18.4%
Common
ValueCountFrequency (%)
1664653
62.5%
1 219344
 
8.2%
2 122328
 
4.6%
3 99528
 
3.7%
5 85915
 
3.2%
7 85542
 
3.2%
6 83568
 
3.1%
4 82339
 
3.1%
8 78632
 
3.0%
9 69935
 
2.6%
Other values (7) 72383
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15256099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2645418
17.3%
1664653
10.9%
T 1253491
 
8.2%
A 1249377
 
8.2%
N 1004665
 
6.6%
R 983565
 
6.4%
S 827156
 
5.4%
U 706147
 
4.6%
V 640384
 
4.2%
O 532151
 
3.5%
Other values (33) 3749092
24.6%

address_type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing215156
Missing (%)18.5%
Memory size8.9 MiB
ADDRESS
880607 
INTERSECTION
 
59672
BLOCKFACE
 
6630
UNRECOGNIZED
 
1200

Length

Max length12
Median length7
Mean length7.3350037
Min length7

Characters and Unicode

Total characters6954383
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADDRESS
2nd rowADDRESS
3rd rowADDRESS
4th rowINTERSECTION
5th rowADDRESS

Common Values

ValueCountFrequency (%)
ADDRESS 880607
75.7%
INTERSECTION 59672
 
5.1%
BLOCKFACE 6630
 
0.6%
UNRECOGNIZED 1200
 
0.1%
(Missing) 215156
 
18.5%

Length

2023-12-05T14:23:36.300280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:36.487142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
address 880607
92.9%
intersection 59672
 
6.3%
blockface 6630
 
0.7%
unrecognized 1200
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 1820886
26.2%
D 1762414
25.3%
E 1008981
14.5%
R 941479
13.5%
A 887237
12.8%
N 121744
 
1.8%
I 120544
 
1.7%
T 119344
 
1.7%
C 74132
 
1.1%
O 67502
 
1.0%
Other values (7) 30120
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6954383
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1820886
26.2%
D 1762414
25.3%
E 1008981
14.5%
R 941479
13.5%
A 887237
12.8%
N 121744
 
1.8%
I 120544
 
1.7%
T 119344
 
1.7%
C 74132
 
1.1%
O 67502
 
1.0%
Other values (7) 30120
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 6954383
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1820886
26.2%
D 1762414
25.3%
E 1008981
14.5%
R 941479
13.5%
A 887237
12.8%
N 121744
 
1.8%
I 120544
 
1.7%
T 119344
 
1.7%
C 74132
 
1.1%
O 67502
 
1.0%
Other values (7) 30120
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6954383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1820886
26.2%
D 1762414
25.3%
E 1008981
14.5%
R 941479
13.5%
A 887237
12.8%
N 121744
 
1.8%
I 120544
 
1.7%
T 119344
 
1.7%
C 74132
 
1.1%
O 67502
 
1.0%
Other values (7) 30120
 
0.4%

city
Categorical

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)< 0.1%
Missing83650
Missing (%)7.2%
Memory size8.9 MiB
BROOKLYN
395150 
BRONX
181092 
NEW YORK
153502 
RIDGEWOOD
 
31234
STATEN ISLAND
 
29233
Other values (44)
289404 

Length

Max length19
Median length8
Mean length8.3471821
Min length5

Characters and Unicode

Total characters9011743
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowBROOKLYN
2nd rowRIDGEWOOD
3rd rowRIDGEWOOD
4th rowBRONX
5th rowBROOKLYN

Common Values

ValueCountFrequency (%)
BROOKLYN 395150
34.0%
BRONX 181092
15.6%
NEW YORK 153502
 
13.2%
RIDGEWOOD 31234
 
2.7%
STATEN ISLAND 29233
 
2.5%
ASTORIA 27850
 
2.4%
LONG ISLAND CITY 19238
 
1.7%
MASPETH 18436
 
1.6%
JAMAICA 18227
 
1.6%
FLUSHING 14643
 
1.3%
Other values (39) 191010
16.4%
(Missing) 83650
 
7.2%

Length

2023-12-05T14:23:36.724055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brooklyn 395151
27.5%
bronx 181093
12.6%
new 153687
 
10.7%
york 153503
 
10.7%
island 48471
 
3.4%
ridgewood 31234
 
2.2%
staten 29233
 
2.0%
astoria 27850
 
1.9%
park 24472
 
1.7%
elmhurst 21210
 
1.5%
Other values (51) 373522
25.9%

Most occurring characters

ValueCountFrequency (%)
O 1437869
16.0%
N 948379
10.5%
R 925816
10.3%
L 616739
 
6.8%
K 598666
 
6.6%
B 592194
 
6.6%
Y 588931
 
6.5%
E 448406
 
5.0%
A 376539
 
4.2%
359811
 
4.0%
Other values (26) 2118393
23.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8651916
96.0%
Space Separator 359811
 
4.0%
Lowercase Letter 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1437869
16.6%
N 948379
11.0%
R 925816
10.7%
L 616739
 
7.1%
K 598666
 
6.9%
B 592194
 
6.8%
Y 588931
 
6.8%
E 448406
 
5.2%
A 376539
 
4.4%
S 287861
 
3.3%
Other values (16) 1830516
21.2%
Lowercase Letter
ValueCountFrequency (%)
o 4
25.0%
r 3
18.8%
n 2
12.5%
k 2
12.5%
x 1
 
6.2%
l 1
 
6.2%
y 1
 
6.2%
e 1
 
6.2%
w 1
 
6.2%
Space Separator
ValueCountFrequency (%)
359811
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8651932
96.0%
Common 359811
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1437869
16.6%
N 948379
11.0%
R 925816
10.7%
L 616739
 
7.1%
K 598666
 
6.9%
B 592194
 
6.8%
Y 588931
 
6.8%
E 448406
 
5.2%
A 376539
 
4.4%
S 287861
 
3.3%
Other values (25) 1830532
21.2%
Common
ValueCountFrequency (%)
359811
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9011743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1437869
16.0%
N 948379
10.5%
R 925816
10.3%
L 616739
 
6.8%
K 598666
 
6.6%
B 592194
 
6.6%
Y 588931
 
6.5%
E 448406
 
5.0%
A 376539
 
4.2%
359811
 
4.0%
Other values (26) 2118393
23.5%

landmark
Text

MISSING 

Distinct6979
Distinct (%)0.6%
Missing83662
Missing (%)7.2%
Memory size8.9 MiB
2023-12-05T14:23:37.186480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.043666
Min length6

Characters and Unicode

Total characters14081981
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique882 ?
Unique (%)0.1%

Sample

1st row23 AVENUE
2nd row64 STREET
3rd row64 STREET
4th rowLELAND AVENUE
5th rowVAN SICLEN AVENUE
ValueCountFrequency (%)
street 505299
21.1%
avenue 382776
 
16.0%
east 88123
 
3.7%
west 76404
 
3.2%
place 43472
 
1.8%
boulevard 37967
 
1.6%
road 36096
 
1.5%
parkway 13432
 
0.6%
drive 11674
 
0.5%
park 11630
 
0.5%
Other values (4036) 1191810
49.7%
2023-12-05T14:23:37.890780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2520033
17.9%
1603320
11.4%
T 1449789
10.3%
A 1021588
 
7.3%
R 1018025
 
7.2%
S 934528
 
6.6%
N 793516
 
5.6%
U 540603
 
3.8%
V 485807
 
3.4%
O 443964
 
3.2%
Other values (33) 3270808
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11461069
81.4%
Space Separator 1603320
 
11.4%
Decimal Number 1017353
 
7.2%
Other Punctuation 151
 
< 0.1%
Dash Punctuation 86
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2520033
22.0%
T 1449789
12.6%
A 1021588
8.9%
R 1018025
8.9%
S 934528
 
8.2%
N 793516
 
6.9%
U 540603
 
4.7%
V 485807
 
4.2%
O 443964
 
3.9%
L 337697
 
2.9%
Other values (16) 1915519
16.7%
Decimal Number
ValueCountFrequency (%)
1 202516
19.9%
2 120227
11.8%
3 103726
10.2%
7 94597
9.3%
6 90837
8.9%
5 89273
8.8%
4 85698
8.4%
8 81930
8.1%
9 80071
 
7.9%
0 68478
 
6.7%
Other Punctuation
ValueCountFrequency (%)
' 149
98.7%
/ 1
 
0.7%
& 1
 
0.7%
Space Separator
ValueCountFrequency (%)
1603320
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11461069
81.4%
Common 2620912
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2520033
22.0%
T 1449789
12.6%
A 1021588
8.9%
R 1018025
8.9%
S 934528
 
8.2%
N 793516
 
6.9%
U 540603
 
4.7%
V 485807
 
4.2%
O 443964
 
3.9%
L 337697
 
2.9%
Other values (16) 1915519
16.7%
Common
ValueCountFrequency (%)
1603320
61.2%
1 202516
 
7.7%
2 120227
 
4.6%
3 103726
 
4.0%
7 94597
 
3.6%
6 90837
 
3.5%
5 89273
 
3.4%
4 85698
 
3.3%
8 81930
 
3.1%
9 80071
 
3.1%
Other values (7) 68717
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14081981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2520033
17.9%
1603320
11.4%
T 1449789
10.3%
A 1021588
 
7.3%
R 1018025
 
7.2%
S 934528
 
6.6%
N 793516
 
5.6%
U 540603
 
3.8%
V 485807
 
3.4%
O 443964
 
3.2%
Other values (33) 3270808
23.2%

status
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Closed
1163065 
In Progress
 
190
Unspecified
 
10

Length

Max length11
Median length6
Mean length6.0008596
Min length6

Characters and Unicode

Total characters6980590
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed
2nd rowClosed
3rd rowClosed
4th rowClosed
5th rowClosed

Common Values

ValueCountFrequency (%)
Closed 1163065
> 99.9%
In Progress 190
 
< 0.1%
Unspecified 10
 
< 0.1%

Length

2023-12-05T14:23:38.151907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:38.344522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
closed 1163065
> 99.9%
in 190
 
< 0.1%
progress 190
 
< 0.1%
unspecified 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 1163455
16.7%
e 1163275
16.7%
o 1163255
16.7%
d 1163075
16.7%
C 1163065
16.7%
l 1163065
16.7%
r 380
 
< 0.1%
n 200
 
< 0.1%
g 190
 
< 0.1%
190
 
< 0.1%
Other values (7) 440
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5816945
83.3%
Uppercase Letter 1163455
 
16.7%
Space Separator 190
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1163455
20.0%
e 1163275
20.0%
o 1163255
20.0%
d 1163075
20.0%
l 1163065
20.0%
r 380
 
< 0.1%
n 200
 
< 0.1%
g 190
 
< 0.1%
i 20
 
< 0.1%
p 10
 
< 0.1%
Other values (2) 20
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 1163065
> 99.9%
P 190
 
< 0.1%
I 190
 
< 0.1%
U 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6980400
> 99.9%
Common 190
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1163455
16.7%
e 1163275
16.7%
o 1163255
16.7%
d 1163075
16.7%
C 1163065
16.7%
l 1163065
16.7%
r 380
 
< 0.1%
n 200
 
< 0.1%
g 190
 
< 0.1%
P 190
 
< 0.1%
Other values (6) 250
 
< 0.1%
Common
ValueCountFrequency (%)
190
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6980590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1163455
16.7%
e 1163275
16.7%
o 1163255
16.7%
d 1163075
16.7%
C 1163065
16.7%
l 1163065
16.7%
r 380
 
< 0.1%
n 200
 
< 0.1%
g 190
 
< 0.1%
190
 
< 0.1%
Other values (7) 440
 
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing239
Missing (%)< 0.1%
Memory size8.9 MiB
The Police Department responded to the complaint and took action to fix the condition.
282696 
The Police Department issued a summons in response to the complaint.
213132 
The Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.
207689 
The Police Department responded to the complaint and determined that police action was not necessary.
199664 
The Police Department responded and upon arrival those responsible for the condition were gone.
197414 
Other values (7)
62431 

Length

Max length185
Median length109
Mean length95.796437
Min length66

Characters and Unicode

Total characters111413747
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThe Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.
2nd rowThe Police Department issued a summons in response to the complaint.
3rd rowThe Police Department issued a summons in response to the complaint.
4th rowThe Police Department issued a summons in response to the complaint.
5th rowThe Police Department responded to the complaint and took action to fix the condition.

Common Values

ValueCountFrequency (%)
The Police Department responded to the complaint and took action to fix the condition. 282696
24.3%
The Police Department issued a summons in response to the complaint. 213132
18.3%
The Police Department responded to the complaint and with the information available observed no evidence of the violation at that time. 207689
17.9%
The Police Department responded to the complaint and determined that police action was not necessary. 199664
17.2%
The Police Department responded and upon arrival those responsible for the condition were gone. 197414
17.0%
The Police Department reviewed your complaint and provided additional information below. 34988
 
3.0%
This complaint does not fall under the Police Department's jurisdiction. 20525
 
1.8%
Your request can not be processed at this time because of insufficient contact information. Please create a new Service Request on NYC.gov and provide more detailed contact information. 5101
 
0.4%
The Police Department responded to the complaint and a report was prepared. 1251
 
0.1%
The Police Department responded to the complaint but officers were unable to gain entry into the premises. 438
 
< 0.1%
Other values (2) 128
 
< 0.1%
(Missing) 239
 
< 0.1%

Length

2023-12-05T14:23:38.630980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 2958817
17.2%
police 1357589
 
7.9%
to 1188100
 
6.9%
department 1137400
 
6.6%
complaint 960511
 
5.6%
and 928835
 
5.4%
responded 889152
 
5.2%
action 482360
 
2.8%
condition 480110
 
2.8%
that 407353
 
2.4%
Other values (73) 6389166
37.2%

Most occurring characters

ValueCountFrequency (%)
16016367
14.4%
e 13022254
11.7%
t 10171267
 
9.1%
o 9815413
 
8.8%
n 8477997
 
7.6%
i 7080956
 
6.4%
a 6377179
 
5.7%
d 4473449
 
4.0%
r 4296068
 
3.9%
p 3864689
 
3.5%
Other values (26) 27818108
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90694145
81.4%
Space Separator 16016367
 
14.4%
Uppercase Letter 3509482
 
3.1%
Other Punctuation 1193753
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13022254
14.4%
t 10171267
11.2%
o 9815413
10.8%
n 8477997
9.3%
i 7080956
 
7.8%
a 6377179
 
7.0%
d 4473449
 
4.9%
r 4296068
 
4.7%
p 3864689
 
4.3%
h 3796931
 
4.2%
Other values (15) 19317942
21.3%
Uppercase Letter
ValueCountFrequency (%)
P 1163026
33.1%
D 1157925
33.0%
T 1157893
33.0%
Y 10234
 
0.3%
S 5101
 
0.1%
R 5101
 
0.1%
N 5101
 
0.1%
C 5101
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1173228
98.3%
' 20525
 
1.7%
Space Separator
ValueCountFrequency (%)
16016367
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94203627
84.6%
Common 17210120
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13022254
13.8%
t 10171267
10.8%
o 9815413
10.4%
n 8477997
 
9.0%
i 7080956
 
7.5%
a 6377179
 
6.8%
d 4473449
 
4.7%
r 4296068
 
4.6%
p 3864689
 
4.1%
h 3796931
 
4.0%
Other values (23) 22827424
24.2%
Common
ValueCountFrequency (%)
16016367
93.1%
. 1173228
 
6.8%
' 20525
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111413747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16016367
14.4%
e 13022254
11.7%
t 10171267
 
9.1%
o 9815413
 
8.8%
n 8477997
 
7.6%
i 7080956
 
6.4%
a 6377179
 
5.7%
d 4473449
 
4.0%
r 4296068
 
3.9%
p 3864689
 
3.5%
Other values (26) 27818108
25.0%
Distinct1139291
Distinct (%)98.0%
Missing143
Missing (%)< 0.1%
Memory size8.9 MiB
Minimum2021-01-01 00:47:12
Maximum2023-12-04 02:20:49
2023-12-05T14:23:38.880200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:39.166648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2023-12-05T14:23:39.384878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length25
Median length21
Mean length10.242158
Min length8

Characters and Unicode

Total characters11914344
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11 BROOKLYN
2nd row05 QUEENS
3rd row05 QUEENS
4th row05 QUEENS
5th row09 BRONX
ValueCountFrequency (%)
brooklyn 420506
17.8%
queens 350901
14.9%
bronx 194686
 
8.3%
manhattan 163830
 
6.9%
05 124005
 
5.3%
01 101883
 
4.3%
02 101616
 
4.3%
12 96374
 
4.1%
07 83325
 
3.5%
10 81969
 
3.5%
Other values (28) 639560
27.1%
2023-12-05T14:23:40.204688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1358003
 
11.4%
1195390
 
10.0%
O 1035698
 
8.7%
0 841607
 
7.1%
E 733927
 
6.2%
B 615192
 
5.2%
R 615192
 
5.2%
1 576718
 
4.8%
A 555740
 
4.7%
L 452631
 
3.8%
Other values (27) 3934246
33.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8312671
69.8%
Decimal Number 2308113
 
19.4%
Space Separator 1195390
 
10.0%
Lowercase Letter 98170
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1358003
16.3%
O 1035698
12.5%
E 733927
8.8%
B 615192
 
7.4%
R 615192
 
7.4%
A 555740
 
6.7%
L 452631
 
5.4%
Y 420506
 
5.1%
K 420506
 
5.1%
S 415151
 
5.0%
Other values (8) 1690125
20.3%
Decimal Number
ValueCountFrequency (%)
0 841607
36.5%
1 576718
25.0%
2 198443
 
8.6%
5 147952
 
6.4%
3 106300
 
4.6%
8 103209
 
4.5%
4 99558
 
4.3%
7 97250
 
4.2%
9 74221
 
3.2%
6 62855
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
e 19634
20.0%
i 19634
20.0%
n 9817
10.0%
s 9817
10.0%
p 9817
10.0%
c 9817
10.0%
f 9817
10.0%
d 9817
10.0%
Space Separator
ValueCountFrequency (%)
1195390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8410841
70.6%
Common 3503503
29.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1358003
16.1%
O 1035698
12.3%
E 733927
8.7%
B 615192
 
7.3%
R 615192
 
7.3%
A 555740
 
6.6%
L 452631
 
5.4%
Y 420506
 
5.0%
K 420506
 
5.0%
S 415151
 
4.9%
Other values (16) 1788295
21.3%
Common
ValueCountFrequency (%)
1195390
34.1%
0 841607
24.0%
1 576718
16.5%
2 198443
 
5.7%
5 147952
 
4.2%
3 106300
 
3.0%
8 103209
 
2.9%
4 99558
 
2.8%
7 97250
 
2.8%
9 74221
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11914344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1358003
 
11.4%
1195390
 
10.0%
O 1035698
 
8.7%
0 841607
 
7.1%
E 733927
 
6.2%
B 615192
 
5.2%
R 615192
 
5.2%
1 576718
 
4.8%
A 555740
 
4.7%
L 452631
 
3.8%
Other values (27) 3934246
33.0%

bbl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct189357
Distinct (%)18.5%
Missing141586
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean2.9321783 × 109
Minimum0
Maximum5.2700005 × 109
Zeros106
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-12-05T14:23:40.454582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0103 × 109
Q12.03886 × 109
median3.04232 × 109
Q34.0163551 × 109
95-th percentile4.1210801 × 109
Maximum5.2700005 × 109
Range5.2700005 × 109
Interquartile range (IQR)1.9774951 × 109

Descriptive statistics

Standard deviation1.0777252 × 109
Coefficient of variation (CV)0.36755104
Kurtosis-0.72782756
Mean2.9321783 × 109
Median Absolute Deviation (MAD)9.8743998 × 108
Skewness-0.38444472
Sum2.995745 × 1015
Variance1.1614915 × 1018
MonotonicityNot monotonic
2023-12-05T14:23:40.737903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3003370027 4261
 
0.4%
4068290001 3058
 
0.3%
3001340006 2087
 
0.2%
2038860002 2064
 
0.2%
2038740063 2059
 
0.2%
3020410001 1972
 
0.2%
3044520423 1900
 
0.2%
2024580026 1569
 
0.1%
1021610001 1523
 
0.1%
2032500046 1506
 
0.1%
Other values (189347) 999680
85.9%
(Missing) 141586
 
12.2%
ValueCountFrequency (%)
0 106
< 0.1%
1000010201 1
 
< 0.1%
1000020001 2
 
< 0.1%
1000020002 40
 
< 0.1%
1000020023 4
 
< 0.1%
1000030001 2
 
< 0.1%
1000030010 8
 
< 0.1%
1000047501 16
 
< 0.1%
1000050010 1
 
< 0.1%
1000057501 10
 
< 0.1%
ValueCountFrequency (%)
5270000501 2
< 0.1%
5240009997 2
< 0.1%
5200459999 1
< 0.1%
5200169999 1
< 0.1%
5200039999 1
< 0.1%
5080500089 1
< 0.1%
5080500078 1
< 0.1%
5080500053 1
< 0.1%
5080500025 2
< 0.1%
5080500007 2
< 0.1%

borough
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
BROOKLYN
420506 
QUEENS
350901 
BRONX
194525 
MANHATTAN
163991 
STATEN ISLAND
 
32125

Length

Max length13
Median length11
Mean length7.177221
Min length5

Characters and Unicode

Total characters8349010
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBROOKLYN
2nd rowQUEENS
3rd rowQUEENS
4th rowQUEENS
5th rowBRONX

Common Values

ValueCountFrequency (%)
BROOKLYN 420506
36.1%
QUEENS 350901
30.2%
BRONX 194525
16.7%
MANHATTAN 163991
 
14.1%
STATEN ISLAND 32125
 
2.8%
Unspecified 1217
 
0.1%

Length

2023-12-05T14:23:41.004224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:41.229076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 420506
35.2%
queens 350901
29.4%
bronx 194525
16.3%
manhattan 163991
 
13.7%
staten 32125
 
2.7%
island 32125
 
2.7%
unspecified 1217
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1358164
16.3%
O 1035537
12.4%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.4%
Y 420506
 
5.0%
K 420506
 
5.0%
S 415151
 
5.0%
Other values (17) 1726303
20.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8304715
99.5%
Space Separator 32125
 
0.4%
Lowercase Letter 12170
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1358164
16.4%
O 1035537
12.5%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.5%
Y 420506
 
5.1%
K 420506
 
5.1%
S 415151
 
5.0%
Other values (8) 1682008
20.3%
Lowercase Letter
ValueCountFrequency (%)
e 2434
20.0%
i 2434
20.0%
n 1217
10.0%
s 1217
10.0%
p 1217
10.0%
c 1217
10.0%
f 1217
10.0%
d 1217
10.0%
Space Separator
ValueCountFrequency (%)
32125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8316885
99.6%
Common 32125
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1358164
16.3%
O 1035537
12.5%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.4%
Y 420506
 
5.1%
K 420506
 
5.1%
S 415151
 
5.0%
Other values (16) 1694178
20.4%
Common
ValueCountFrequency (%)
32125
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8349010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1358164
16.3%
O 1035537
12.4%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.4%
Y 420506
 
5.0%
K 420506
 
5.0%
S 415151
 
5.0%
Other values (17) 1726303
20.7%

x_coordinate_state_plane
Real number (ℝ)

HIGH CORRELATION 

Distinct86282
Distinct (%)7.5%
Missing8818
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1005924.5
Minimum913444
Maximum1067281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-12-05T14:23:41.482205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum913444
5-th percentile980246
Q1991567
median1005207
Q31019202
95-th percentile1041052
Maximum1067281
Range153837
Interquartile range (IQR)27635

Descriptive statistics

Standard deviation20689.199
Coefficient of variation (CV)0.020567347
Kurtosis1.3975799
Mean1005924.5
Median Absolute Deviation (MAD)13819
Skewness-0.21523733
Sum1.1612865 × 1012
Variance4.2804294 × 108
MonotonicityNot monotonic
2023-12-05T14:23:41.766438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
984166 4273
 
0.4%
1037000 3066
 
0.3%
1045818 2342
 
0.2%
987324 2253
 
0.2%
1020596 2068
 
0.2%
988901 2056
 
0.2%
987322 2032
 
0.2%
1019422 1739
 
0.1%
1011682 1655
 
0.1%
1003772 1548
 
0.1%
Other values (86272) 1131415
97.3%
(Missing) 8818
 
0.8%
ValueCountFrequency (%)
913444 1
 
< 0.1%
913469 1
 
< 0.1%
913503 1
 
< 0.1%
913526 1
 
< 0.1%
913533 3
< 0.1%
913586 2
< 0.1%
913596 1
 
< 0.1%
913613 1
 
< 0.1%
913712 2
< 0.1%
913754 1
 
< 0.1%
ValueCountFrequency (%)
1067281 1
 
< 0.1%
1067180 1
 
< 0.1%
1067173 1
 
< 0.1%
1067166 1
 
< 0.1%
1067131 2
 
< 0.1%
1067126 1
 
< 0.1%
1067125 4
< 0.1%
1067124 1
 
< 0.1%
1067123 5
< 0.1%
1067120 2
 
< 0.1%

y_coordinate_state_plane
Real number (ℝ)

HIGH CORRELATION 

Distinct105519
Distinct (%)9.1%
Missing8623
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean203243.9
Minimum121316
Maximum271876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-12-05T14:23:42.020329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum121316
5-th percentile156819
Q1183156
median199404
Q3222639
95-th percentile256961
Maximum271876
Range150560
Interquartile range (IQR)39483

Descriptive statistics

Standard deviation30517.865
Coefficient of variation (CV)0.1501539
Kurtosis-0.70698976
Mean203243.9
Median Absolute Deviation (MAD)19731
Skewness0.23256208
Sum2.3467394 × 1011
Variance9.3134007 × 108
MonotonicityNot monotonic
2023-12-05T14:23:42.307316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188346 4268
 
0.4%
202363 3077
 
0.3%
210166 2339
 
0.2%
192647 2073
 
0.2%
242945 2064
 
0.2%
177846 1717
 
0.1%
250722 1529
 
0.1%
191913 1419
 
0.1%
264242 1396
 
0.1%
243713 1368
 
0.1%
Other values (105509) 1133392
97.4%
(Missing) 8623
 
0.7%
ValueCountFrequency (%)
121316 1
 
< 0.1%
121374 1
 
< 0.1%
121519 1
 
< 0.1%
121544 1
 
< 0.1%
121586 3
< 0.1%
121677 1
 
< 0.1%
121702 1
 
< 0.1%
121730 1
 
< 0.1%
121750 1
 
< 0.1%
121755 1
 
< 0.1%
ValueCountFrequency (%)
271876 40
< 0.1%
271730 10
 
< 0.1%
271676 50
< 0.1%
271667 1
 
< 0.1%
271664 1
 
< 0.1%
271641 2
 
< 0.1%
271628 1
 
< 0.1%
271622 1
 
< 0.1%
271621 2
 
< 0.1%
271598 1
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
MOBILE
527056 
ONLINE
430820 
PHONE
205225 
UNKNOWN
 
124
OTHER
 
40

Length

Max length7
Median length6
Mean length5.8236507
Min length5

Characters and Unicode

Total characters6774449
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowONLINE
2nd rowONLINE
3rd rowONLINE
4th rowONLINE
5th rowMOBILE

Common Values

ValueCountFrequency (%)
MOBILE 527056
45.3%
ONLINE 430820
37.0%
PHONE 205225
 
17.6%
UNKNOWN 124
 
< 0.1%
OTHER 40
 
< 0.1%

Length

2023-12-05T14:23:42.600222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:42.834982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
mobile 527056
45.3%
online 430820
37.0%
phone 205225
 
17.6%
unknown 124
 
< 0.1%
other 40
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 1163265
17.2%
E 1163141
17.2%
N 1067237
15.8%
I 957876
14.1%
L 957876
14.1%
M 527056
7.8%
B 527056
7.8%
H 205265
 
3.0%
P 205225
 
3.0%
U 124
 
< 0.1%
Other values (4) 328
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6774449
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1163265
17.2%
E 1163141
17.2%
N 1067237
15.8%
I 957876
14.1%
L 957876
14.1%
M 527056
7.8%
B 527056
7.8%
H 205265
 
3.0%
P 205225
 
3.0%
U 124
 
< 0.1%
Other values (4) 328
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6774449
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1163265
17.2%
E 1163141
17.2%
N 1067237
15.8%
I 957876
14.1%
L 957876
14.1%
M 527056
7.8%
B 527056
7.8%
H 205265
 
3.0%
P 205225
 
3.0%
U 124
 
< 0.1%
Other values (4) 328
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6774449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1163265
17.2%
E 1163141
17.2%
N 1067237
15.8%
I 957876
14.1%
L 957876
14.1%
M 527056
7.8%
B 527056
7.8%
H 205265
 
3.0%
P 205225
 
3.0%
U 124
 
< 0.1%
Other values (4) 328
 
< 0.1%

park_facility_name
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Unspecified
1163263 
Police Museum
 
1
121 PRECINCT
 
1

Length

Max length13
Median length11
Mean length11.000003
Min length11

Characters and Unicode

Total characters12795918
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUnspecified
2nd rowUnspecified
3rd rowUnspecified
4th rowUnspecified
5th rowUnspecified

Common Values

ValueCountFrequency (%)
Unspecified 1163263
> 99.9%
Police Museum 1
 
< 0.1%
121 PRECINCT 1
 
< 0.1%

Length

2023-12-05T14:23:43.116776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:43.328568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
unspecified 1163263
> 99.9%
police 1
 
< 0.1%
museum 1
 
< 0.1%
121 1
 
< 0.1%
precinct 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 2326528
18.2%
i 2326527
18.2%
s 1163264
9.1%
c 1163264
9.1%
U 1163263
9.1%
n 1163263
9.1%
p 1163263
9.1%
f 1163263
9.1%
d 1163263
9.1%
C 2
 
< 0.1%
Other values (14) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11632640
90.9%
Uppercase Letter 1163273
 
9.1%
Decimal Number 3
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2326528
20.0%
i 2326527
20.0%
s 1163264
10.0%
c 1163264
10.0%
n 1163263
10.0%
p 1163263
10.0%
f 1163263
10.0%
d 1163263
10.0%
u 2
 
< 0.1%
l 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
U 1163263
> 99.9%
C 2
 
< 0.1%
P 2
 
< 0.1%
M 1
 
< 0.1%
R 1
 
< 0.1%
E 1
 
< 0.1%
I 1
 
< 0.1%
N 1
 
< 0.1%
T 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12795913
> 99.9%
Common 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2326528
18.2%
i 2326527
18.2%
s 1163264
9.1%
c 1163264
9.1%
U 1163263
9.1%
n 1163263
9.1%
p 1163263
9.1%
f 1163263
9.1%
d 1163263
9.1%
C 2
 
< 0.1%
Other values (11) 13
 
< 0.1%
Common
ValueCountFrequency (%)
1 2
40.0%
2
40.0%
2 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12795918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2326528
18.2%
i 2326527
18.2%
s 1163264
9.1%
c 1163264
9.1%
U 1163263
9.1%
n 1163263
9.1%
p 1163263
9.1%
f 1163263
9.1%
d 1163263
9.1%
C 2
 
< 0.1%
Other values (14) 18
 
< 0.1%

park_borough
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
BROOKLYN
420506 
QUEENS
350901 
BRONX
194525 
MANHATTAN
163991 
STATEN ISLAND
 
32125

Length

Max length13
Median length11
Mean length7.177221
Min length5

Characters and Unicode

Total characters8349010
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBROOKLYN
2nd rowQUEENS
3rd rowQUEENS
4th rowQUEENS
5th rowBRONX

Common Values

ValueCountFrequency (%)
BROOKLYN 420506
36.1%
QUEENS 350901
30.2%
BRONX 194525
16.7%
MANHATTAN 163991
 
14.1%
STATEN ISLAND 32125
 
2.8%
Unspecified 1217
 
0.1%

Length

2023-12-05T14:23:43.597664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:23:43.835987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 420506
35.2%
queens 350901
29.4%
bronx 194525
16.3%
manhattan 163991
 
13.7%
staten 32125
 
2.7%
island 32125
 
2.7%
unspecified 1217
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1358164
16.3%
O 1035537
12.4%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.4%
Y 420506
 
5.0%
K 420506
 
5.0%
S 415151
 
5.0%
Other values (17) 1726303
20.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8304715
99.5%
Space Separator 32125
 
0.4%
Lowercase Letter 12170
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1358164
16.4%
O 1035537
12.5%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.5%
Y 420506
 
5.1%
K 420506
 
5.1%
S 415151
 
5.0%
Other values (8) 1682008
20.3%
Lowercase Letter
ValueCountFrequency (%)
e 2434
20.0%
i 2434
20.0%
n 1217
10.0%
s 1217
10.0%
p 1217
10.0%
c 1217
10.0%
f 1217
10.0%
d 1217
10.0%
Space Separator
ValueCountFrequency (%)
32125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8316885
99.6%
Common 32125
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1358164
16.3%
O 1035537
12.5%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.4%
Y 420506
 
5.1%
K 420506
 
5.1%
S 415151
 
5.0%
Other values (16) 1694178
20.4%
Common
ValueCountFrequency (%)
32125
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8349010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1358164
16.3%
O 1035537
12.4%
E 733927
8.8%
B 615031
 
7.4%
R 615031
 
7.4%
A 556223
 
6.7%
L 452631
 
5.4%
Y 420506
 
5.0%
K 420506
 
5.0%
S 415151
 
5.0%
Other values (17) 1726303
20.7%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct284474
Distinct (%)24.6%
Missing8838
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean40.724477
Minimum40.499409
Maximum40.912869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-12-05T14:23:44.093483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum40.499409
5-th percentile40.597021
Q140.669323
median40.71392
Q340.77768
95-th percentile40.871925
Maximum40.912869
Range0.41346008
Interquartile range (IQR)0.10835694

Descriptive statistics

Standard deviation0.083757439
Coefficient of variation (CV)0.0020566854
Kurtosis-0.70651246
Mean40.724477
Median Absolute Deviation (MAD)0.054189086
Skewness0.23263872
Sum47013436
Variance0.0070153086
MonotonicityNot monotonic
2023-12-05T14:23:44.386695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.68364303 4261
 
0.4%
40.72195913 3059
 
0.3%
40.74331941 2337
 
0.2%
40.83342849 2061
 
0.2%
40.69544706 2051
 
0.2%
40.65475302 1717
 
0.1%
40.85482728 1520
 
0.1%
40.69343309 1403
 
0.1%
40.89187242 1396
 
0.1%
40.83554666 1361
 
0.1%
Other values (284464) 1133261
97.4%
(Missing) 8838
 
0.8%
ValueCountFrequency (%)
40.49940871 1
 
< 0.1%
40.49956978 1
 
< 0.1%
40.49996957 1
 
< 0.1%
40.50002606 1
 
< 0.1%
40.50014121 3
< 0.1%
40.50039682 1
 
< 0.1%
40.50046544 1
 
< 0.1%
40.50054219 1
 
< 0.1%
40.50057897 1
 
< 0.1%
40.50062032 1
 
< 0.1%
ValueCountFrequency (%)
40.9128688 40
< 0.1%
40.91246817 10
 
< 0.1%
40.91231946 50
< 0.1%
40.91229474 1
 
< 0.1%
40.91228642 1
 
< 0.1%
40.91222312 2
 
< 0.1%
40.91218664 1
 
< 0.1%
40.91217017 1
 
< 0.1%
40.91216807 2
 
< 0.1%
40.91210338 1
 
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct284471
Distinct (%)24.6%
Missing8838
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean-73.921767
Minimum-74.254624
Maximum-73.700376
Zeros0
Zeros (%)0.0%
Negative1154427
Negative (%)99.2%
Memory size8.9 MiB
2023-12-05T14:23:44.637887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-74.254624
5-th percentile-74.014426
Q1-73.973613
median-73.924364
Q3-73.873805
95-th percentile-73.795137
Maximum-73.700376
Range0.55424813
Interquartile range (IQR)0.099808523

Descriptive statistics

Standard deviation0.074627547
Coefficient of variation (CV)-0.0010095477
Kurtosis1.3832865
Mean-73.921767
Median Absolute Deviation (MAD)0.04990471
Skewness-0.21307527
Sum-85337283
Variance0.0055692708
MonotonicityNot monotonic
2023-12-05T14:23:44.917748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.00030287 4261
 
0.4%
-73.80969682 3059
 
0.3%
-73.77781335 2337
 
0.2%
-73.86865697 2061
 
0.2%
-73.98322754 2051
 
0.2%
-73.87323986 1717
 
0.1%
-73.92943095 1520
 
0.1%
-73.98891486 1403
 
0.1%
-73.86016845 1396
 
0.1%
-73.87797279 1361
 
0.1%
Other values (284461) 1133261
97.4%
(Missing) 8838
 
0.8%
ValueCountFrequency (%)
-74.25462445 1
 
< 0.1%
-74.25453162 1
 
< 0.1%
-74.25440803 1
 
< 0.1%
-74.25433153 1
 
< 0.1%
-74.2543014 3
< 0.1%
-74.25411077 2
< 0.1%
-74.25407768 1
 
< 0.1%
-74.25401918 1
 
< 0.1%
-74.25365765 2
< 0.1%
-74.2535115 1
 
< 0.1%
ValueCountFrequency (%)
-73.70037632 1
 
< 0.1%
-73.70073668 1
 
< 0.1%
-73.7007611 1
 
< 0.1%
-73.70078552 1
 
< 0.1%
-73.70090631 2
< 0.1%
-73.70092367 1
 
< 0.1%
-73.70092713 3
< 0.1%
-73.70092798 1
 
< 0.1%
-73.70093144 1
 
< 0.1%
-73.70093408 4
< 0.1%
Distinct284474
Distinct (%)24.6%
Missing8838
Missing (%)0.8%
Memory size8.9 MiB
2023-12-05T14:23:45.621808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length141
Median length140
Mean length140.05185
Min length127

Characters and Unicode

Total characters161679641
Distinct characters38
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156427 ?
Unique (%)13.6%

Sample

1st row{'latitude': '40.59842551366083', 'longitude': '-73.99386035358324', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
2nd row{'latitude': '40.703605926745354', 'longitude': '-73.89356421159655', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
3rd row{'latitude': '40.7038420696995', 'longitude': '-73.89366482112014', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
4th row{'latitude': '40.702568005237964', 'longitude': '-73.89313307748259', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
5th row{'latitude': '40.840250548425054', 'longitude': '-73.86515232413545', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
ValueCountFrequency (%)
4617708
30.8%
latitude 1154427
 
7.7%
state 1154427
 
7.7%
longitude 1154427
 
7.7%
zip 1154427
 
7.7%
human_address 1154427
 
7.7%
address 1154427
 
7.7%
city 1154427
 
7.7%
40.68364302931577 4261
 
< 0.1%
74.00030286766025 4261
 
< 0.1%
Other values (568943) 2300332
15.3%
2023-12-05T14:23:46.601109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 18470832
 
11.4%
13853124
 
8.6%
' 13853124
 
8.6%
: 8080989
 
5.0%
t 6926562
 
4.3%
d 6926562
 
4.3%
a 5772135
 
3.6%
e 5772135
 
3.6%
, 5772135
 
3.6%
s 5772135
 
3.6%
Other values (28) 70479908
43.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55412496
34.3%
Other Punctuation 48485934
30.0%
Decimal Number 37001525
22.9%
Space Separator 13853124
 
8.6%
Open Punctuation 2308854
 
1.4%
Close Punctuation 2308854
 
1.4%
Dash Punctuation 1154427
 
0.7%
Connector Punctuation 1154427
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 6926562
12.5%
d 6926562
12.5%
a 5772135
10.4%
e 5772135
10.4%
s 5772135
10.4%
i 4617708
8.3%
u 3463281
 
6.2%
n 2308854
 
4.2%
r 2308854
 
4.2%
l 2308854
 
4.2%
Other values (8) 9235416
16.7%
Decimal Number
ValueCountFrequency (%)
7 4632740
12.5%
4 4358671
11.8%
3 4047455
10.9%
0 4034998
10.9%
8 3640261
9.8%
9 3632288
9.8%
6 3545179
9.6%
5 3158313
8.5%
2 2991985
8.1%
1 2959635
8.0%
Other Punctuation
ValueCountFrequency (%)
" 18470832
38.1%
' 13853124
28.6%
: 8080989
16.7%
, 5772135
 
11.9%
. 2308854
 
4.8%
Space Separator
ValueCountFrequency (%)
13853124
100.0%
Open Punctuation
ValueCountFrequency (%)
{ 2308854
100.0%
Close Punctuation
ValueCountFrequency (%)
} 2308854
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1154427
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1154427
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 106267145
65.7%
Latin 55412496
34.3%

Most frequent character per script

Common
ValueCountFrequency (%)
" 18470832
17.4%
13853124
13.0%
' 13853124
13.0%
: 8080989
 
7.6%
, 5772135
 
5.4%
7 4632740
 
4.4%
4 4358671
 
4.1%
3 4047455
 
3.8%
0 4034998
 
3.8%
8 3640261
 
3.4%
Other values (10) 25522816
24.0%
Latin
ValueCountFrequency (%)
t 6926562
12.5%
d 6926562
12.5%
a 5772135
10.4%
e 5772135
10.4%
s 5772135
10.4%
i 4617708
8.3%
u 3463281
 
6.2%
n 2308854
 
4.2%
r 2308854
 
4.2%
l 2308854
 
4.2%
Other values (8) 9235416
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161679641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 18470832
 
11.4%
13853124
 
8.6%
' 13853124
 
8.6%
: 8080989
 
5.0%
t 6926562
 
4.3%
d 6926562
 
4.3%
a 5772135
 
3.6%
e 5772135
 
3.6%
, 5772135
 
3.6%
s 5772135
 
3.6%
Other values (28) 70479908
43.6%

Interactions

2023-12-05T14:22:59.610872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:41.087581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:43.744258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:46.275814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:49.747032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:53.163438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:56.434054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:00.098194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:41.470085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:44.189233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:46.610299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:50.213766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:53.667195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:56.862072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:00.500968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:41.803590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:44.486400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:46.938293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:50.683492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:54.081069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:57.352277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:00.938147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:42.226281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:44.834472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:47.378155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:51.140114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:54.559986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:57.821149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:01.431642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:42.602914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:45.189583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:47.840525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:51.610581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:55.022137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:58.295821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:01.880900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:42.985723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:45.528279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:48.357030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:52.112227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:55.476271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:58.731891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:23:02.319551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:43.348817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:45.887852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:49.115261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:52.623830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:55.885699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:22:59.185896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-05T14:23:46.827785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
address_typebblboroughcitydescriptorincident_ziplatitudelongitudeopen_data_channel_typepark_boroughpark_facility_nameresolution_descriptionstatusunique_keyx_coordinate_state_planey_coordinate_state_plane
address_type1.000-0.0020.0350.6130.1050.0250.0110.0610.1430.0351.0000.0220.001-0.0040.0610.012
bbl-0.0021.0000.8920.6650.1120.795-0.4870.3630.0470.8920.0050.0650.006-0.0040.363-0.486
borough0.0350.8921.0000.8940.1490.392-0.1750.1640.0441.0000.0030.0850.007-0.0050.165-0.175
city0.6130.6650.8941.0000.1210.193-0.189-0.0360.0850.8940.0000.0870.011-0.017-0.036-0.188
descriptor0.1050.1120.1490.1211.000-0.027-0.010-0.0100.1470.1490.0000.0970.006-0.018-0.010-0.010
incident_zip0.0250.7950.3920.193-0.0271.000-0.4440.4520.0570.6830.0000.0620.004-0.0020.453-0.444
latitude0.011-0.487-0.175-0.189-0.010-0.4441.0000.4070.0520.5700.0010.0650.004-0.0170.4061.000
longitude0.0610.3630.164-0.036-0.0100.4520.4071.0000.0580.5810.0070.0590.007-0.0501.0000.407
open_data_channel_type0.1430.0470.0440.0850.1470.0570.0520.0581.0000.0440.0000.0320.000-0.0050.046-0.024
park_borough0.0350.8921.0000.8940.1490.6830.5700.5810.0441.0000.0030.0850.007-0.0050.165-0.175
park_facility_name1.0000.0050.0030.0000.0000.0000.0010.0070.0000.0031.0000.0000.0000.0020.0000.000
resolution_description0.0220.0650.0850.0870.0970.0620.0650.0590.0320.0850.0001.0000.125-0.034-0.0460.050
status0.0010.0060.0070.0110.0060.0040.0040.0070.0000.0070.0000.1251.0000.0190.0080.003
unique_key-0.004-0.004-0.005-0.017-0.018-0.002-0.017-0.050-0.005-0.0050.002-0.0340.0191.000-0.050-0.017
x_coordinate_state_plane0.0610.3630.165-0.036-0.0100.4530.4061.0000.0460.1650.000-0.0460.008-0.0501.0000.406
y_coordinate_state_plane0.012-0.486-0.175-0.188-0.010-0.4441.0000.407-0.024-0.1750.0000.0500.003-0.0170.4061.000

Missing values

2023-12-05T14:23:03.809105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T14:23:07.486224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-05T14:23:17.352222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

unique_keycreated_dateclosed_dateagencyagency_namecomplaint_typedescriptorlocation_typeincident_zipincident_addressstreet_namecross_street_1cross_street_2intersection_street_1intersection_street_2address_typecitylandmarkstatusresolution_descriptionresolution_action_updated_datecommunity_boardbblboroughx_coordinate_state_planey_coordinate_state_planeopen_data_channel_typepark_facility_namepark_boroughlatitudelongitudelocation
0529343482021-12-31T23:58:51.0002022-01-01T01:00:41.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11214.08700 23 AVENUE23 AVENUEBENSON AVENUEBATH AVENUEBENSON AVENUEBATH AVENUEADDRESSBROOKLYN23 AVENUEClosedThe Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.2022-01-01T01:00:44.00011 BROOKLYN3.064170e+09BROOKLYN985955.0157299.0ONLINEUnspecifiedBROOKLYN40.598426-73.993860{'latitude': '40.59842551366083', 'longitude': '-73.99386035358324', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1529385132021-12-31T23:58:36.0002022-01-01T02:36:38.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11385.069-41 64 STREET64 STREETCATALPA AVENUESHALER AVENUECATALPA AVENUESHALER AVENUEADDRESSRIDGEWOOD64 STREETClosedThe Police Department issued a summons in response to the complaint.2022-01-01T02:36:42.00005 QUEENS4.036320e+09QUEENS1013761.0195637.0ONLINEUnspecifiedQUEENS40.703606-73.893564{'latitude': '40.703605926745354', 'longitude': '-73.89356421159655', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
2529377002021-12-31T23:56:20.0002022-01-01T02:36:19.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11385.069-23 64 STREET64 STREETCATALPA AVENUESHALER AVENUECATALPA AVENUESHALER AVENUEADDRESSRIDGEWOOD64 STREETClosedThe Police Department issued a summons in response to the complaint.2022-01-01T02:36:24.00005 QUEENS4.036320e+09QUEENS1013733.0195723.0ONLINEUnspecifiedQUEENS40.703842-73.893665{'latitude': '40.7038420696995', 'longitude': '-73.89366482112014', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
3529316612021-12-31T23:54:13.0002022-01-01T02:35:59.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11385.064 STREET64 STREET64 STREETSHALER AVENUE64 STREETSHALER AVENUEINTERSECTIONNaNNaNClosedThe Police Department issued a summons in response to the complaint.2022-01-01T02:36:04.00005 QUEENSNaNQUEENS1013881.0195259.0ONLINEUnspecifiedQUEENS40.702568-73.893133{'latitude': '40.702568005237964', 'longitude': '-73.89313307748259', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
4529327182021-12-31T23:51:25.0002022-01-01T01:30:40.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk10460.01577 LELAND AVENUELELAND AVENUEGUERLAIN STREETEAST TREMONT AVENUEGUERLAIN STREETEAST TREMONT AVENUEADDRESSBRONXLELAND AVENUEClosedThe Police Department responded to the complaint and took action to fix the condition.2022-01-01T01:30:46.00009 BRONX2.039260e+09BRONX1021562.0245432.0MOBILEUnspecifiedBRONX40.840251-73.865152{'latitude': '40.840250548425054', 'longitude': '-73.86515232413545', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
5529320002021-12-31T23:46:54.0002022-01-01T01:17:46.000NYPDNew York City Police DepartmentIllegal ParkingBlocked CrosswalkStreet/Sidewalk11207.077 VAN SICLEN AVENUEVAN SICLEN AVENUEARLINGTON AVENUEFULTON STREETARLINGTON AVENUEFULTON STREETADDRESSBROOKLYNVAN SICLEN AVENUEClosedThe Police Department responded to the complaint and took action to fix the condition.2022-01-01T01:17:55.00005 BROOKLYN3.039330e+09BROOKLYN1014258.0186533.0MOBILEUnspecifiedBROOKLYN40.678616-73.891812{'latitude': '40.67861589367691', 'longitude': '-73.8918122624042', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
6529357502021-12-31T23:43:23.0002022-01-01T02:11:03.000NYPDNew York City Police DepartmentIllegal ParkingPosted Parking Sign ViolationStreet/Sidewalk11378.071-44 58 AVENUE58 AVENUE73 STREET73 PLACE73 STREET73 PLACEADDRESSMASPETH58 AVENUEClosedThe Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.2022-01-01T02:11:08.00005 QUEENS4.028190e+09QUEENS1015086.0203863.0MOBILEUnspecifiedQUEENS40.726180-73.888748{'latitude': '40.72617979417831', 'longitude': '-73.88874772806905', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
7529380712021-12-31T23:41:21.0002022-01-01T01:10:36.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11226.025 PARADE PLACEPARADE PLACEWOODRUFF AVENUECROOKE AVENUEWOODRUFF AVENUECROOKE AVENUEADDRESSBROOKLYNPARADE PLACEClosedThe Police Department responded and upon arrival those responsible for the condition were gone.2022-01-01T01:10:40.00014 BROOKLYN3.050580e+09BROOKLYN993777.0176961.0MOBILEUnspecifiedBROOKLYN40.652389-73.965666{'latitude': '40.652388617370235', 'longitude': '-73.96566585079294', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
8529321922021-12-31T23:41:12.0002022-01-01T00:35:53.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11234.02068 EAST 61 STREETEAST 61 STREETAVENUE TAVENUE UAVENUE TAVENUE UADDRESSBROOKLYNEAST 61 STREETClosedThe Police Department responded to the complaint and took action to fix the condition.2022-01-01T00:35:55.00018 BROOKLYN3.084030e+09BROOKLYN1007755.0163534.0ONLINEUnspecifiedBROOKLYN40.615508-73.915338{'latitude': '40.615508300006354', 'longitude': '-73.91533762905237', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
9529353712021-12-31T23:33:08.0002022-01-01T01:14:18.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11435.0139-11 87 DRIVE87 DRIVE139 STREET143 STREET139 STREET143 STREETADDRESSJAMAICA87 DRIVEClosedThe Police Department responded and upon arrival those responsible for the condition were gone.2022-01-01T01:14:21.00008 QUEENS4.096990e+09QUEENS1035623.0196313.0MOBILEUnspecifiedQUEENS40.705361-73.814711{'latitude': '40.70536146628257', 'longitude': '-73.81471070155054', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
unique_keycreated_dateclosed_dateagencyagency_namecomplaint_typedescriptorlocation_typeincident_zipincident_addressstreet_namecross_street_1cross_street_2intersection_street_1intersection_street_2address_typecitylandmarkstatusresolution_descriptionresolution_action_updated_datecommunity_boardbblboroughx_coordinate_state_planey_coordinate_state_planeopen_data_channel_typepark_facility_namepark_boroughlatitudelongitudelocation
1163255564142292023-01-01T00:19:59.0002023-01-01T00:26:06.000NYPDNew York City Police DepartmentIllegal ParkingDouble Parked Blocking TrafficStreet/Sidewalk10305.051 ANDREWS STREETANDREWS STREETOLYMPIA BOULEVARDQUINCY AVENUEOLYMPIA BOULEVARDQUINCY AVENUEADDRESSSTATEN ISLANDANDREWS STREETClosedThe Police Department issued a summons in response to the complaint.2023-01-01T00:26:12.00002 STATEN ISLAND5.034100e+09STATEN ISLAND964125.0154621.0MOBILEUnspecifiedSTATEN ISLAND40.591052-74.072461{'latitude': '40.59105225933984', 'longitude': '-74.07246143998023', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163256564163652023-01-01T00:19:48.0002023-01-01T05:51:47.000NYPDNew York City Police DepartmentIllegal ParkingCommercial Overnight ParkingStreet/Sidewalk11366.0196-69 73 AVENUE73 AVENUE196 PLACE197 STREET196 PLACE197 STREETADDRESSFRESH MEADOWS73 AVENUEClosedThe Police Department responded to the complaint and determined that police action was not necessary.2023-01-01T05:51:55.00008 QUEENS4.071170e+09QUEENS1046424.0207305.0ONLINEUnspecifiedQUEENS40.735462-73.775653{'latitude': '40.73546245678656', 'longitude': '-73.77565287060753', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163257564149922023-01-01T00:16:28.0002023-01-01T01:07:56.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk10462.01944 UNIONPORT ROADUNIONPORT ROADSAGAMORE STREETBRONX PARK EASTSAGAMORE STREETBRONX PARK EASTADDRESSBRONXUNIONPORT ROADClosedThe Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.2023-01-01T01:08:01.00011 BRONX2.042560e+09BRONX1020522.0248895.0PHONEUnspecifiedBRONX40.849760-73.868892{'latitude': '40.84975978935839', 'longitude': '-73.86889220514804', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163258564167572023-01-01T00:16:18.0002023-01-01T00:41:27.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11354.033-30 149 PLACE149 PLACE33 AVENUE34 AVENUE33 AVENUE34 AVENUEADDRESSFLUSHING149 PLACEClosedThe Police Department responded to the complaint and took action to fix the condition.2023-01-01T00:41:33.00007 QUEENS4.049890e+09QUEENS1035097.0219319.0ONLINEUnspecifiedQUEENS40.768510-73.816434{'latitude': '40.7685101446476', 'longitude': '-73.81643396042371', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163259564183752023-01-01T00:14:05.0002023-01-01T00:55:18.000NYPDNew York City Police DepartmentIllegal ParkingCommercial Overnight ParkingStreet/Sidewalk11236.010516 AVENUE KAVENUE KEAST 105 STREETEAST 108 STREETEAST 105 STREETEAST 108 STREETADDRESSBROOKLYNAVENUE KClosedThe Police Department responded to the complaint and determined that police action was not necessary.2023-01-01T00:55:24.00018 BROOKLYN3.082510e+09BROOKLYN1014650.0174142.0MOBILEUnspecifiedBROOKLYN40.644604-73.890455{'latitude': '40.64460402376489', 'longitude': '-73.8904548539346', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163260564158962023-01-01T00:11:43.0002023-01-01T01:19:17.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11418.0102-41 86 ROAD86 ROAD102 STREETDEAD END102 STREETDEAD ENDADDRESSRICHMOND HILL86 ROADClosedThe Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.2023-01-01T01:19:24.00009 QUEENS4.091860e+09QUEENS1027282.0192796.0MOBILEUnspecifiedQUEENS40.695753-73.844817{'latitude': '40.69575263417357', 'longitude': '-73.84481696249556', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163261564142642023-01-01T00:06:11.0002023-01-01T03:30:43.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11378.060-24 56 ROAD56 ROAD60 STREET61 STREET60 STREET61 STREETADDRESSMASPETH56 ROADClosedThe Police Department issued a summons in response to the complaint.2023-01-01T03:30:47.00005 QUEENS4.027040e+09QUEENS1010402.0203235.0MOBILEUnspecifiedQUEENS40.724471-73.905649{'latitude': '40.7244711757953', 'longitude': '-73.90564941620535', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163262564168062023-01-01T00:03:42.0002023-01-01T03:02:12.000NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11378.060-65 55 STREET55 STREETNURGE AVENUEARNOLD AVENUENURGE AVENUEARNOLD AVENUEADDRESSMASPETH55 STREETClosedThe Police Department responded to the complaint and took action to fix the condition.2023-01-01T03:02:17.00005 QUEENS4.026410e+09QUEENS1009096.0199714.0MOBILEUnspecifiedQUEENS40.714811-73.910374{'latitude': '40.71481064814987', 'longitude': '-73.91037416673439', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163263564182512023-01-01T00:01:50.0002023-01-01T02:31:47.000NYPDNew York City Police DepartmentIllegal ParkingBlocked CrosswalkStreet/Sidewalk11230.0BAY AVENUEBAY AVENUEBAY AVENUEEAST 19 STREETBAY AVENUEEAST 19 STREETINTERSECTIONNaNNaNClosedThe Police Department issued a summons in response to the complaint.2023-01-01T02:31:51.00014 BROOKLYNNaNBROOKLYN996427.0164395.0MOBILEUnspecifiedBROOKLYN40.617894-73.956138{'latitude': '40.61789437610167', 'longitude': '-73.95613825034788', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
1163264564187952023-01-01T00:00:45.0002023-01-01T01:24:10.000NYPDNew York City Police DepartmentIllegal ParkingPosted Parking Sign ViolationStreet/Sidewalk10001.015 HUDSON BOULEVARDHUDSON BOULEVARDWEST 33 STREETWEST 34 STREETWEST 33 STREETWEST 34 STREETADDRESSNEW YORKHUDSON BOULEVARD EASTClosedThe Police Department responded to the complaint and determined that police action was not necessary.2023-01-01T01:24:16.00004 MANHATTAN1.007050e+09MANHATTAN984043.0214298.0MOBILEUnspecifiedMANHATTAN40.754875-74.000747{'latitude': '40.75487501846257', 'longitude': '-74.00074715055744', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}